ef6abf3dc7
Adds ALL files needed for Dockerfile build:
- qwen3_6_scripts/ (baseline patches + our optimizations)
- vllm/ (full vllm package)
- paged_attention_v2_pytorch.py (V2 with single-bmm optimization)
- Dockerfile + computility-run.yaml
Our optimizations vs baseline:
1. paged_attn.py: pre-gathered context KV (eliminates 194 gather calls),
Triton try/fallback, V2 heuristic, threshold 32K→64K
2. paged_attention_v2_pytorch.py: fills NotImplementedError,
single-bmm Phase 1 (195 launches → 3)
3. patch_enable_triton.py: HAS_TRITON=True with safety fallback
4. patch_triton_tuning.py: BLOCK=64, NUM_WARPS=4 for BI-V100
5. computility-run.yaml: gpu-memory-utilization 0.9→0.95,
max-num-batched-tokens 8192→16384
This repo can now be submitted to dev.modelhub.org.cn as-is.
100 lines
4.0 KiB
Python
100 lines
4.0 KiB
Python
from typing import List, Optional
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from vllm.config import TokenizerPoolConfig
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from vllm.lora.request import LoRARequest
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from vllm.transformers_utils.tokenizer import (AnyTokenizer,
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get_lora_tokenizer,
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get_lora_tokenizer_async,
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get_tokenizer)
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from vllm.utils import LRUCache
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from .base_tokenizer_group import BaseTokenizerGroup
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class TokenizerGroup(BaseTokenizerGroup):
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"""A group of tokenizers that can be used for LoRA adapters."""
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def __init__(self, tokenizer_id: str, enable_lora: bool, max_num_seqs: int,
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max_input_length: Optional[int], **tokenizer_config):
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self.tokenizer_id = tokenizer_id
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self.tokenizer_config = tokenizer_config
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self.enable_lora = enable_lora
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self.max_input_length = max_input_length
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self.tokenizer = get_tokenizer(self.tokenizer_id, **tokenizer_config)
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self.lora_tokenizers = LRUCache[AnyTokenizer](
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capacity=max_num_seqs if enable_lora else 0)
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@classmethod
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def from_config(cls, tokenizer_pool_config: Optional[TokenizerPoolConfig],
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**init_kwargs) -> "TokenizerGroup":
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return cls(**init_kwargs)
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def ping(self) -> bool:
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"""Check if the tokenizer group is alive."""
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return True
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def get_max_input_len(self,
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lora_request: Optional[LoRARequest] = None
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) -> Optional[int]:
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"""Get the maximum input length for the LoRA request."""
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return self.max_input_length
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def _raise_if_input_too_long(self,
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encoded_tokens: List[int],
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lora_request: Optional[LoRARequest] = None):
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input_length = len(encoded_tokens)
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if lora_request:
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max_input_length = (lora_request.long_lora_max_len
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or self.max_input_length)
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else:
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max_input_length = self.max_input_length
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if max_input_length is not None and input_length > max_input_length:
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raise ValueError("Input too long.", input_length, max_input_length)
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def encode(self,
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prompt: str,
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request_id: Optional[str] = None,
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lora_request: Optional[LoRARequest] = None) -> List[int]:
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tokenizer = self.get_lora_tokenizer(lora_request)
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ret = tokenizer.encode(prompt)
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self._raise_if_input_too_long(ret, lora_request)
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return ret
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async def encode_async(
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self,
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prompt: str,
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request_id: Optional[str] = None,
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lora_request: Optional[LoRARequest] = None) -> List[int]:
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tokenizer = await self.get_lora_tokenizer_async(lora_request)
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ret = tokenizer.encode(prompt)
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self._raise_if_input_too_long(ret, lora_request)
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return ret
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def get_lora_tokenizer(
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self,
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lora_request: Optional[LoRARequest] = None,
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) -> AnyTokenizer:
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if not lora_request or not self.enable_lora:
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return self.tokenizer
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if lora_request.lora_int_id not in self.lora_tokenizers:
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tokenizer = (get_lora_tokenizer(
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lora_request, **self.tokenizer_config) or self.tokenizer)
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self.lora_tokenizers.put(lora_request.lora_int_id, tokenizer)
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return tokenizer
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else:
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return self.lora_tokenizers[lora_request.lora_int_id]
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async def get_lora_tokenizer_async(
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self,
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lora_request: Optional[LoRARequest] = None,
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) -> AnyTokenizer:
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if not lora_request or not self.enable_lora:
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return self.tokenizer
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if lora_request.lora_int_id not in self.lora_tokenizers:
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tokenizer = (await get_lora_tokenizer_async(
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lora_request, **self.tokenizer_config) or self.tokenizer)
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self.lora_tokenizers.put(lora_request.lora_int_id, tokenizer)
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return tokenizer
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else:
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return self.lora_tokenizers[lora_request.lora_int_id]
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